{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#!/usr/bin/env python3\n",
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "import numpy as np\n",
    "from scipy import signal\n",
    "from pylab import *\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False #用来正常显示负号\n",
    "fs = 256\n",
    "\n",
    "def rd(filename):\n",
    "    # filename = 'X:/3_2 运动算法资料/4_data/data.dat'\n",
    "    fid = open(filename, 'rb')\n",
    "    fid1 = fid.read()\n",
    "\n",
    "    data = []\n",
    "    ecg = []\n",
    "    acc = []\n",
    "    acc_x = []\n",
    "    acc_y = []\n",
    "    acc_z = []\n",
    "    #根据.dat写入格式，将两个8位组合成一个数据\n",
    "    for i in range(0, len(fid1), 2):\n",
    "        if fid1[i] >> 7 == 1:\n",
    "            data.append(((fid1[i] - pow(2, 8)) << 8) + fid1[i+1])\n",
    "        else:\n",
    "          data.append((fid1[i] << 8) + fid1[i+1])\n",
    "    fid.close()\n",
    "\n",
    "    #根据数据存储格式，将数组切片，前5位为ecg数据，后3位为三轴加速度数据\n",
    "    for i in range(0,len(data),8):\n",
    "        for j in range(5):\n",
    "            ecg.append(data[i+j])\n",
    "        acc_x.append(data[i+5])\n",
    "        acc_y.append(data[i+6])\n",
    "        acc_z.append(data[i+7])\n",
    "        acc.append(np.linalg.norm( [data[i+5], data[i+6], data[i+7]], ord=2))#加速度二范数\n",
    "\n",
    "    return ecg, acc, acc_x, acc_y, acc_z\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# butter带通滤波\n",
    "def butter_bandpass(data, fre, lowcut, highcut, order=3):\n",
    "    nyq = 0.5 * fre\n",
    "    low = lowcut / nyq\n",
    "    high = highcut / nyq\n",
    "    b, a = signal.butter(order, [low, high], btype='band')\n",
    "    new_sig = signal.filtfilt(b, a, data)\n",
    "    return new_sig\n",
    "# 对于丢数片段，进行插值处理，权重向量计算\n",
    "def interpolat(acc):\n",
    "    #以下求权重向量w_i\n",
    "    n = fs #需要拟合的点的数量\n",
    "    w_ii = [1/(n-ii) for ii in range(n)]\n",
    "    w_i = [x / sum(w_ii) for x in w_ii]\n",
    "    for num in range(len(acc)):\n",
    "        if acc[num] == 0:\n",
    "            if num > fs:\n",
    "                acc_i = acc[num-n: num]\n",
    "            else:\n",
    "                acc_i = acc[num+1: num+n+1]\n",
    "            acc[num] = sum(np.multiply(w_i, acc_i))\n",
    "    return acc\n",
    "\n",
    "# # 设置动态阈值\n",
    "def thr_mean(acc_norm): # 其中acc_norm长度为1分钟的倍数\n",
    "    acc_norm2 = acc_norm.reshape(t, 1, 3072) #创建3维矩阵\n",
    "    acc_mean_t = [np.mean(acc_norm2[tt, :, :]) for tt in range(t)]\n",
    "    acc_mean = np.dot(np.transpose([acc_mean_t]), ones((1,3072))).reshape(t*3072)\n",
    "    plt.plot(acc_norm)\n",
    "    plt.plot(acc_mean)\n",
    "    plt.show()\n",
    "    return acc_mean\n",
    "\n",
    "# 通过积分方法PIM计算活动量\n",
    "# 具体算法1：以每分钟为计数单位，将每分钟分为6个10s片段，求6个片段中的积分最大值\n",
    "def pim(acc_norm, thr):\n",
    "    acc_norm = abs(acc_norm - thr)\n",
    "    acc_norm = acc_norm.reshape(t, 6, 512) #创建3维矩阵\n",
    "    # 计算每个epoch=10s的积分，并求最大\n",
    "    delte = 1/(fs/5)\n",
    "    acc_pim = []\n",
    "    for epoch in range(t):\n",
    "        acc_epoch = delte*sum(acc_norm[epoch, :, i] for i in range(512))\n",
    "        acc_pim.append(max(acc_epoch))\n",
    "    \n",
    "    return acc_pim\n",
    "\n",
    "# 过零检测模式ZCM来计算活动计数\n",
    "def zcm(acc_norm, thr_zcm):\n",
    "    thr_zcm = thr_zcm*ones(len(acc_norm))\n",
    "    acc_zcm = acc_norm - thr_zcm\n",
    "    acc_zcm = acc_zcm.reshape(t,3072) #3072为1min的采样点\n",
    "    zcm_count = []\n",
    "    for tt in range(t):\n",
    "        c=0\n",
    "        for sam in range(3071):\n",
    "            if acc_zcm[tt,sam]*acc_zcm[tt,sam+1]<0:\n",
    "                c+=1\n",
    "        zcm_count.append(c)\n",
    "    return zcm_count\n",
    "\n",
    "#   Webster's算法\n",
    "def webster(acc_act):\n",
    "    D = list(zeros(4)) #暂时将前4分钟及后2分钟赋值为0，后面会再次赋值\n",
    "    for i in range(4, t-2):\n",
    "        w_d = array([0.15, 0.15, 0.15, 0.08, 0.21, 0.12, 0.13])\n",
    "        a_i = array(acc_act[i-4: i+3])\n",
    "        d_i = 0.025*sum(w_d * a_i)\n",
    "        D.append(d_i)\n",
    "    D = D + list(zeros(2))\n",
    "    return D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 批量读取数据\n",
    "##当使用固定阈值1g时，th_sleep=28,th_deep=16 此阈值设置偏高，易把久坐不动判断为睡眠状态\n",
    "# th_sleep=18,th_deep=5  此阈值设置偏低，易把睡眠状态判断为清醒\n",
    "#当使用动态阈值acc_mean时,th_sleep=5,th_deep=1 仍将部分久坐判断为睡眠，那么考虑频谱分析\n",
    "# path = \"F:/工作任务/睡眠监测/data/CA-30-F3-FF-FE-CD2-9/9403\"#文件夹目录 \n",
    "# path = \"F:/工作任务/睡眠监测/data/2018/2/10/FC-00-00-00-00-4F/43509\" \n",
    "# path = \"F:/工作任务/睡眠监测/data/2018/2/4/FC-00-00-00-00-4F/43284\" \n",
    "# path = \"F:/工作任务/睡眠监测/data/FC-00-00-00-00-4F/9442\" \n",
    "# path = \"F:/工作任务/睡眠监测/data/FC-00-00-00-00-4F/9443\" \n",
    "# path = \"F:/工作任务/睡眠监测/data/FC-00-00-00-00-4F/9445\" \n",
    "# path = \"F:/工作任务/睡眠监测/data/FC-00-00-00-00-4F/9454\" \n",
    "path = \"F:/工作任务/睡眠监测/data/14/FC-00-00-00-00-4F/9701\"\n",
    "# path = \"F:/工作任务/睡眠监测/data/2018/3/21/FC-00-00-00-00-4F/9859\"\n",
    "# path = \"F:/工作任务/睡眠监测/data/2018/3/21/FC-00-00-00-00-4F/9860\"\n",
    "# path = \"F:/工作任务/睡眠监测/data/2018/3/21/FC-00-00-00-00-4F/9862\"\n",
    "# path = \"X:/3_2 运动算法资料/4_data/Text2018-02-06/FC-00-00-00-00-4F/43285\"\n",
    "\n",
    "\n",
    "\n",
    "files = os.listdir(path) #得到文件夹下的所有文件名称\n",
    "acc_norm = []\n",
    "acc_x = []\n",
    "acc_y = []\n",
    "acc_z = []\n",
    "for file in files:#遍历文件夹\n",
    "    file = os.path.join(path, file)\n",
    "    if os.path.isdir(file): #判断是否是文件夹，是文件夹才打开\n",
    "        filename = file + \"/\" + \"data.dat\" #data.dat文件目录\n",
    "        data = rd(filename)[1] #调用data_reading模块，返回加速度二范数（共5个返回值中的第2个）\n",
    "#         data_x = rd(filename)[2]\n",
    "#         data_y = rd(filename)[3]\n",
    "#         data_z = rd(filename)[4]\n",
    "        \n",
    "        acc_norm.extend(data)\n",
    "#         acc_x.extend(data_x)\n",
    "#         acc_y.extend(data_y)\n",
    "#         acc_z.extend(data_z)\n",
    "        \n",
    "\n",
    "# 读取单个文件数据\n",
    "# filename = \"F:/工作任务/睡眠监测/data/CA-30-F3-FF-FE-CD2-9/9403/18-02-09 05-33-20/data.dat\"\n",
    "# acc_norm = data_reading.rd(filename)[1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将加速度数据存为txt，移植c用\n",
    "# f = open(path+\"/\" +'43285.txt', 'w')\n",
    "# f.write(str(acc_norm))\n",
    "# f.close\n",
    "\n",
    "np.savetxt(path+\"/\" +'43285.txt', acc_norm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01:34:44\n",
      "94\n",
      "5684\n"
     ]
    }
   ],
   "source": [
    "# 读取起始文件名，获得监测时间\n",
    "\n",
    "def t2s(t):\n",
    "    h,m,s = t.strip().split(\":\")\n",
    "    return int(h) * 3600 + int(m) * 60 + int(s)\n",
    "def t2m(t):\n",
    "    h,m,s = t.strip().split(\":\")\n",
    "    return int(h) * 60 + int(m) \n",
    "def s2t(seconds):\n",
    "    m, s = divmod(seconds, 60)\n",
    "    h, m = divmod(m, 60)\n",
    "    return \"%02d:%02d:%02d\" % (h, m, s)\n",
    "def m2t(minutes):\n",
    "    h, m = divmod(minutes, 60)\n",
    "    s = 0\n",
    "    if h >=24:\n",
    "        h-=24\n",
    "    return \"%02d:%02d\" % (h, m)\n",
    "\n",
    "t_start = files[0][9:].replace('-',':')\n",
    "m_start = t2m(t_start)\n",
    "s_start = t2s(t_start)\n",
    "print(t_start)\n",
    "print(m_start)\n",
    "print(s_start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HX7Z0Pus27WHGmELufOZ9Lp02nC/c90rUY44b3Idr5k7guofeYFj/3uz0Fk+44ORizjux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\ni4gEgJK9iEgA/H8pP/YeMHW64AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119d5978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axis_s = range(m_start*3072,m_start*3072+len(acc_norm))\n",
    "\n",
    "plt.xticks([tt for tt in range(m_start*3072,m_start*3072+len(acc_norm),3072*60)],\n",
    "  [m2t(tt/3072) for tt in range(m_start*3072,m_start*3072+len(acc_norm),3072*60)])\n",
    "\n",
    "plt.title(\"raw acc_norm\")\n",
    "plt.plot(axis_s, acc_norm)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lCZepPftEat4sj8nXjuGq40MPETzpsFKeuvxoLh7dKyToa+XlGc2b5XHW8O6B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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfd394e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对于丢数的片段（最长不超过5s），通过插值法补数\n",
    "\n",
    "acc_norm = interpolat(acc_norm)\n",
    "\n",
    "plt.title(\"acc_norm after interpolation\")\n",
    "plt.xticks([tt for tt in range(m_start*3072,m_start*3072+len(acc_norm),3072*60)],\n",
    "  [m2t(tt/3072) for tt in range(m_start*3072,m_start*3072+len(acc_norm),3072*60)])\n",
    "\n",
    "\n",
    "plt.plot(axis_s, acc_norm)\n",
    "plt.show()\n",
    "\n",
    "# plt.title(\"acc_x\")\n",
    "# plt.plot(interpolat(acc_x))\n",
    "# plt.show()\n",
    "# plt.title(\"acc_y\")\n",
    "# plt.plot(interpolat(acc_y))\n",
    "# plt.show()\n",
    "# plt.title(\"acc_z\")\n",
    "# plt.plot(interpolat(acc_z))\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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x/oimZj8UeEZVJ6jqBCADGAeMBDaLyCQRGVG/LFYBG2OMab5oTpcwGjhNRCYC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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14cde7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 进行butter带通滤波\n",
    "data = acc_norm\n",
    "fre = 51.2\n",
    "lowcut = 0.5\n",
    "highcut = 11\n",
    "acc_norm_butter = butter_bandpass(data, fre, lowcut, highcut)\n",
    "\n",
    "plt.title(\"acc_butter through butter filter\")\n",
    "plt.xticks([tt for tt in range(m_start*3072,m_start*3072+len(acc_norm),3072*30)],\n",
    "  [m2t(tt/3072) for tt in range(m_start*3072,m_start*3072+len(acc_norm),3072*30)])\n",
    "\n",
    "plt.plot(axis_s, acc_norm)\n",
    "plt.plot(axis_s, acc_norm_butter)\n",
    "plt.show()\n",
    "\n",
    "acc_norm = acc_norm_butter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 活动计数\n",
    "\n",
    "# 通过积分方法PIM计算活动量\n",
    "# 具体算法1：以每分钟为计数单位，将每分钟分为6个10s片段，求6个片段中的积分最大值\n",
    "num_epoch = int(len(acc_norm)/(2*fs)) #10 s片段数量\n",
    "t = int(num_epoch/6) #1 minute片段数量\n",
    "acc_norm = array(acc_norm[:int(12*fs*t)])\n",
    "\n",
    "\n",
    "# 通过积分方法PIM计算活动量\n",
    "thr_h = 0 #设置固定阈值为0\n",
    "# thr = thr_mean(acc_norm)\n",
    "thr = thr_h*ones(len(acc_norm))#带通滤波后，选择0为阈值\n",
    "acc_pim = pim(acc_norm, thr)\n",
    "\n",
    "# 通过过零点ZCM计算活动量\n",
    "thr_zcm = 30*ones(len(acc_norm))\n",
    "acc_zcm = zcm(acc_norm, thr_zcm)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Udk/E/7eyJ75YXinLbBCSbAgiYl+3Tr67dLHHirIxYjflZKOwE1EpEX1DRNO9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\nR4HDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14ce1b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TE7/+eli3rvO6ZY1OOfTQMC0nHhj0/GsV8ViceKwiLieeMayDkt93Y2Ji+vQg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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14d01588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#更新横坐标\n",
    "axis_m = range(m_start,t+m_start)\n",
    "# axis_t = [m2t(a) for a in axis_m]\n",
    "\n",
    "# ax1=plt.subplot(111)\n",
    "# xmajorLocator = MultipleLocator(60)\n",
    "# ax.xaxis.set_major_locator(xmajorLocator) \n",
    "\n",
    "# 做映射，设置x刻度：用axis_t来显示刻度\n",
    "plt.title(\"score of each minute by PIM(red)/ZCM(blue) \")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "\n",
    "plt.plot(axis_m, acc_pim, 'r')\n",
    "plt.plot(axis_m, acc_zcm, 'b')\n",
    "# plt.savefig(\"活动量-PIM\")\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"score of each minute by ZCM(blue) \")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "\n",
    "plt.plot(axis_m, acc_zcm, 'b')\n",
    "# plt.savefig(\"活动量-PIM\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f2c/DLx6mNRzIyjJK25Iq9nVLss5q48TylMYtRC0ieytShramAM1Bf8oTd+qb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\nGwfGn0icxU5yJ3jl4pR2KCc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WYNX8GVx15kIuOambSMzdYuPO++ieEebmddvZdnCQf79q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\npcMd0Yw6SloAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfd39860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IV2wczw8SWsuJ7KMfsKEQexvZSzIbJ0MuTmwv/jjPvlVUXiz75NzFCdpoZJ93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\nBKjYK4qiDAH/PxonGuZoXFtvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bcdd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#   Webster's算法 D = 0.025(0.15A_4 + 0.15A_3 + 0.15A_2 + 0.08A_l + 0.21Ao + 0.12A+l + 0.13A+2)\n",
    "# acc_act = acc_pim #积分法计数获得的按分钟活动量\n",
    "# acc_act = acc_zcm #过零点法计数获得的按分钟活动量\n",
    "                     \n",
    "D_pim = webster(acc_pim)\n",
    "D_zcm = webster(acc_zcm)\n",
    "\n",
    "plt.title(\"Webster's score by PIM\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, D_pim)\n",
    "plt._show()\n",
    "\n",
    "plt.title(\"Webster's score by ZCM\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, D_zcm)\n",
    "plt._show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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crGDb2tHQGCsa3Xy7Tf+KvZ0/Gycusv/nG3bVHWe0qHPuFrKsEUDb/XGWKjZK\nloGhgomy7Xr2YxGx76SNQm5LLzVcVGXHBBtWnI3YpJKmElPRs2GiBMsgnL5m2H/OT4g6DoaRrtpL\nLRDCrQiSCeNTy1V/F7ECR/b5Ra7WzFNkb8fU2cfRarl6twi2Omi32mW5bGO4aKJgkp+gHSnWNp0G\nOvuM5lfc5e2dlId2ExlbtErQmhouqnIj+3rLpn5RX+Mqoqpv49Rec9rqETz0uz9V1+LYPT796zhY\npDFUCJcyjxSjnr1GH0iX6VuxlwKSp+Zbbm+c1mJvZJSgDVbjtBM5H55dwlLFxnDBRNE04Qhgbqnq\nR3jS1+1M7Dtf+NVNlPvZy0VVWkX2Tp1/fuWOdTh7aiz0nGkYDUVaft8KkZtGUOgBwMxQXIMaMGSZ\noTUAI5H8UT/vYNX3Yp+naVmcZx+HlfEKWiC5Z/+jF07irX/5HWxZNexOnb1o6sRiGedtctsa1Bay\ndGLj6FWNY8dsSxiHnqWX9TZi3BoI02icWJY3gWCCNo5ChpG9tJYmhwtYO1YMiX3NxpE5hPzoRVL6\nX+xzdKe2Y6bRcRhZraD1/PX5lSpWqg4Wy1W8NF/2x7B5cqjh6uSZUysAgEMnl/CyTRMhsY8maMvV\n9v8vulXjqNbZZ729pApxnn0cpmE0HLf83kVLiKMEE7RpI22cz/zcK3DepnG8cGLJ/5lcVGUNQJ19\n34q9FJA8fXhunb3Cl40ok+nvYtnGqpGCJ/Y2/stnvo1nZhb8n//+z5zfcPVr0CcdDvikFVtgVHr2\nXbFxNK3GUW5xnPqQlImL7ONoFtn7Nk6L1dbBBG3ayOtr/UQJ68eHcHRuxf9ZzbOX16JGH0iX6dtF\nVXn17E2NErQL5SrWjLo1kcsVBwePL+EN563Hp//rxTAImJ5bbvja4HkfLpqh3bbqSi+7IPba2TgK\nomlkuJewClW7flFfHFaTahwZqRdb2DhZbrgeLQeN9ey7YCnqTt+LfZ4+PFXPvtVOQd1iccX2xX5u\nqYKy7eDSM1bjZ195GkqW2bQVhfyZZRDGSlaoAqK7pZe1nvtZrsZsRC2yb31sVttLqhJX+huHSp19\nSxsnQ3GV+SZ5vQWvRd+zt7KzlXpF39o4eUzQ2rbal83KSCQWylWsGXHFfmbenfrKviJFy2iatJUC\n/lfvuhRnT43iiel5/2cjxWhk30lvnHASeSjtFTotsIWo6y/TiKzWS6gS12I7juZi7yVoFW2cbBK0\ntTbbwd8N1GwcGWTlyQlISt9G9rU6e32+TK1QjuwzSNAKIbBUtrHai+yPeQnXCa+vSKv+9PJnl21b\ng+3rx8PRVClSZ9/BDXlhpep/DmUnAAAdcUlEQVR/eXWwcmyndSWOxKBsGtqp0s3IvlULBCvD0kt5\nfcnrLbi6Nxp4cGSfQ+QHnKc7tWo1TqudgrpB2XZQdQRWj7iRvIzsZROpkqLYS5GPtXE6TNDajsBS\nxcamySEcnl3Gsfky3nnLAzi+4FYMjRRNfOEDr8bpa7Jrce0I0bJVgsQVzXTHk4S4FttxNNsDWYp3\ny8g+y0VVkYVeoUVVXuBheluA5sn2TUrfRvb59OwVq3FSTtCWq44vmNJvl6WU40MBG6fJuS1HfdIm\nCdp2b8gyObt2zJ19PDF9Co9Pn8K5G8dxyemrcOClRTx9dL7ZW3Qd2xEJInvdFlUJpQStjOzjciSy\nCq6VHSRnEFksDqxZS+T9Xe/ZA+5sI4vqoF7Rv5F9zsReCLc1rNI0OuUE7f/4lwfx6OE5AMBIyULJ\nMnBMevZS7M0WkX1kOh+XFCt26NnL5Oza0RIA4IhXHfRLu8/G6pEi7nz0SNs9fdrFdkTL1bMSI6PF\ncapUHQelQmtJCFbSREU9rhFaHFnWtZcjNk6oGiewsrdoGm5Tvj6ljyP7fHn2UrxVV9CmmaA9PLuE\nHx9z6+lHiiZKlukvplK2cWwHxUAf9FKMZ99p6eVCJLI/csoV+8nhgn9z6WSz9HZwhJrvDWRjxyUh\niWcPxC8IU7ZxMk7QFsxa0jwuQQvIDVXyERy2Q99H9nnZqUpOZ1WiQsMgf7/QNAgK5GjRjezl+MYT\nJGiDtfXxpZedefbSxlk35kb2M95imUmvYgjIfrvCJDYOkWaefYJ2HUB8jXxF0caRdlEWNk656oRu\nPkRuj/6y7fiWIuDeoPLiBLRDH0f2+bJxkkT2zRJk3SBY1TJSNDFUcC8TgwIWjGU09drLVSck8LGe\nvWX4x7aDbG+8LiayL3kbVGRt47gJWsXI3tBtUVXCyD5O7B1VG6e2cU3aVGynrnNnwXQTssEZZ8Gg\n3DgB7dDHkX2+2iXEbfbcCDdBm95YVqoOdp6xGqMlC+duHPeFc6xk+UJWNA3MLlUavkdU7IMtb+Um\nFp149p//7gG85CWRpWc/PbsM01vEJVBbbJUlSSJ73WycuG0x42gq9nIxXSuxz7QaR9TZStJiDK6H\nKFj9Hdn3rdiX/cheny9TMxJH9ilGRCtVBzs2jOMPrr0QAFDyIntZiQMo2Dh2fGQ/WjT9G0Ynnv3v\nf+1Rf3azbtwV+6NzK5gYcve3LfXIs7cdtRs24Nk4Gol9Ys8+Ruxl0NKqzj7LDb7ldoTR3x9df2dl\n1GCwV7CNowlyOmu2iIiA5otausGKt7uURD6Wfr37XOt2CcUYzz7okborTZN/RhVvDYDUybXewq9T\nK1Xfr5e/uxcJ2iR19lrZOMpdLxuLvWojNGnjZNUbp97GMULXonwuT+tyktK3kX3e9qBNFNmnLPbL\nkbYD8vHEcILIPpIUkzeMsZgvWNLZV3SlrEzQArXkrIzue1F6qWzjGKRV10vlyJ4a18jLBG0rsZe/\nJ4s9X2U1TpCSZdRtqFIwjb7uZ68UgxDRrUR0PxHdqHoMEVlE9DwR3eP9ubBbg1Yhb6WXcvqovFw9\npem/EMKtpImJ7CcCkX3LBG0DG0euWAw+n/SGLHvXv3zTBM7bOI6J4dq4gjekkmVkX42TIEFLTbb3\n6wVub5zOPPuq44Co9XUsu0xmIa7RwANwhX2kTuwHPEFLRNcCMIUQu4jo74hohxDiqVbHABgH8E9C\niN9MZ+iNEUIEPPt83KmTRvZpWfbS9pA+PQA/QRvy7M0WjdAiCVrDIFgGhVYsAvILllTs3Wj9F67Y\nhut2nh6yQoJll6WCmb2NkzRBq1For755SXMbp1VUDwSqcTJqlxC1ccaGLEwOFyNj4gTtbgC3eY/v\nBPBaAE8pHDMM4KeJ6PUAHgLwQSFEFRkQnF7mrc5edRqdVsmaL/ZWLeqJi+xVFlVFLZuiZTSwcZL9\nX6Q1I+0lw6jVTU9GI/uMbRzVDUAAaePoI/ZV2+l8UZUtWpZdAsENvrOyccJj+qN3XIiiWT/LXCxn\nIlE9QcXGGQVwyHt8HMAGxWO+D+AqIcRlAAoA3hx9ERHdQER7iWjvzMxM0rE3JCgeeblT1yJ7tW0J\nHYFU+rfLjbtDNk6jahy7cQ/5RhUQsUmxhEvUpY0zFBijXAtQL/bZR/Yqm8YD+i2qUo3s5TUaW3pp\nO8ptkoHsbJzotbh9/Ti2rg03yHNX0Opz8+02KmI/DzdKB4CxBq+JO+ZHQojD3nN7AeyIvkgIcYsQ\nYqcQYufU1FSigTcj2N8iLx6cX43T4QrGTpHiGErQ+jZOwLM3DQjRODKL2jgAsGlyCKetHg49V2yj\ntlluQRhMsMnxhsXe7Ilnrx7Z69UIraq4U5rUzXixr69pj8PKNEEr6q7FOAot+j3lHRWx3wfXlgGA\niwEcUDzmC0R0MRGZAN4GYH9HI01AOYeRvXRlOp1Gd4q0PVpF9vK5RpFznE9624d24SNXhe/5nXj2\ncRVDYc++N9U4yitotVtUpb7hONA4si8ovAcReXXt2aygbVX3D7jXYj9H9iqe/VcA7CGizQDeBOCd\nRHSTEOLGJsdcDuBHAL4IgAB8VQjxze4OvTFB8chL3ayM7JMkyNKw7aVFEq7GkaWX4cge8HIiJdQR\nVwExEbhZSNrx7Gs2TlDs622cVknkNHCEgIKuAJA2jh7iIoRQ30+haWTvhFZLNyOrvZTjrsU4+r30\nsqXYCyHmiGg3gKsBfEoIMY1IlB5zzCyAWQAXdX3ECuTZs09W5+wA6O42fLVqnPoEbdizd3/eaNob\nt5AlDnchS3t19kOFoGcfF9mbTVs6pIFqrTrgfta6BPbJqsEaJ1dVN0Bxf1fyNRbtEDfL7OV4eoXS\noiohxAnUqm3aPiYr5Ac2UjRz05+6muDLZqQY2csEbTj5GePZt2hithKTFIvD7SHeBRvHql/41api\nKA0cB8oJWoOyWUGqQtJqMKCz0ksgu5bCFVvxWrSSW4p5oi/bJcgPbKRo5ubDSxLZ+02kUviiNIvs\no4uqAKBsx3vi0YVZjSi08QVbjlsL0LAap7Vnv1yx8b1nX8JDL8wmGkccSRK0hka9ceT1p+JtN11U\npRhFAxlG9oo2jjuefOhFO/Rlu4Sa2Fs4sVju8WjU8CN7hS+bkXGCdtfZa3HtpVuwdc2o/1yz3jNy\nUZuqjTO/nKy2eblcH9nLTpqTI8mqcZbKNq75s3tx8PgSJoYs7P+da0KdEJOS1MbRRVxqkb36gqhG\n1TiqNk7BTLehnyRJNQ43QssZ+YzsZemletlaKjaOv6iqNo4z1o7i5usuCX1hSk1sHNmkTHVxTbue\n/XCkGscgYCywQtetxml+kh748Us4eHwJV2xfi7nlKl6cXcan73gC23/rG7jwd+7AY972jKok2anK\n0ChBm8SzlzZVXLCRxMYxM+oyqTqmgkm5Kehoh74Ue7lIZ7Rk5SbhIi961RbHQDo2jhTSUrT/a4Rm\nYu/v+akQTbXVG6dqwzSobi/RyeFCqOxRxca578kZlCwDH3rd2QCAJ4+cwh2PTGPj5BBOrVTxw+dP\nJhpbog3HDYIul2d76zxibvQJxL5gGqnX2cteT0Wl0kujr0sv+1Lsg5G97QhtoqdmJPHsU03QykVV\nLYS65tk3FnvVHintlF5Gx/e+K87EH74jXPxVslr3xrnvyRm8+qy1uHDLJABg/8GTeGZmHm9/xRYU\nTQPPvbSQaGzJ6uzTWQXdDkl7M7mvqf+Zu6hKtRon/Tp7Kd5K1TheKahO/Yq6SV+LvWy6lQcrJ0k1\nTqoJWlln3yKy9zf0jvHE5flW9UnbqcYZiozv3I3j+KnzN4aek9U4jQT1hROLeGZmAVfuWIdVI0VM\njZfw1QdfhCOAi05bhdPXDONAQrF36+zzZ+Mk7boKxEf2brsE1Wqc9CNpeS2q1tkDta0V+42+FnvZ\nTjcPYt9WZJ9RgjaOZpH9SgIbpz3P3qkT+zharfLd89QxAMDuc91WHedsGMOzx1xxv3DLJM5YO4rn\nXlpMNLYkCVoj5X0JkpCkQMDva9NgUZVKrgbIJrJPMsuUM5K8WL9J6Uuxl+JRi+z1//Bqkb16gjaN\nqGi54sCg1jOM0AraCPIGoFJ6WWzHxqnaobLLRtQ2HY9//6eOzGO0aOLsqTEAwI714wDczVA2TJRw\nxtoRPH98MZHV4ggkapegiYuTqECg1baEyjZOBu0JyglnmUA2zdl6QV+KvbQF8hTZS59QZUs7o8mi\nlk5ZqdooWWbL8sNmi6r8BK3i1Dmx2JftUKuERtT2oY1P0h6eXcKmVcP+/3XHBlf0L9wyASLCtrWj\nWCzbmJlfUR6bm6BVO9Yw9Nm8pB0bsdGG46o2TiGDunYZ6Klci3Lc/VqR059in2vPXj2ySitBO6QQ\nNfuefZMErVI01UbXy+WqnWyMDWrtX5xdxqbJIf/f52xwI/sLvGStbIGbxMpJkqA1NNq8JEk1WLNg\no6zY9RLIpjeOb+NYrf9fxQw3VOkFfS32ctuxPNg4dhulb2klaEsqUbPZuDdO0qlzxRaJrJLlilO3\nf2jsGK3mnv3hk0shsb9g8yRef+4U3nzhJgDAtrXuIrIDx9STtEkStDptXmIn8OybLaqqOmodJgG5\nqCybBG10o5I4ZKCVh+CwHfpyBa3v2ZfyGNn3PkGr5IcXWts4KhGeH00l8HqXKzZWDdd30Kwbo+/Z\n19s45aqDmfkVbJqs9dcfLpr43++7zP/3llXDMA3C88eTRfZ5bJeQZAWt2WRRVUWxNQEg69qzStAq\n1NlbUuz1+Ey6zUBE9nnYkMCvxlGphqD0ppvLFbWeNioJ2iRJsSQ35LjSyzia3ZCOzC1DCGDzqqG6\nn0mKloHNq4ZwIIGN4yTYcNy1cZTfOlXaq7OPEXtHKM0O5O9K2zKR16JK22XZhz8PwWE79KfYV/Ps\n2fd+8xIVIZUbiDeKmgH1BC2ARN1JlyuOYjVOYxvn8OwyAIQi+zhOXz2CF04kjOwTdL3UxcZJsoK2\nqdgnKL3MYsWq1IJSkn1xObLPDxVHgKjWKCsP07JE/exTTtCqRPaAtw9tExtHretl8goI5ci+Senl\n4dklAM0jewBYP17CsaTVOAkaoelSZ9+NyN72eiKpFBnI90m7zFF+91UiezkjyaIa56kjp/D00VOp\n/54g/Sn2Xn+OopVtwsVxBO59cqatJfC1agj1apy0WhyrJGiB2qbjUZL1xkk+dV6uJCy9rNTPPl48\n6Ub2G1tE9uvGSpg5taL8mToiQT97b+N4HWhnBW00Kl8ou91Lh4vq1ThpB2KyBbda/ii7OvuP/utD\n+NiXH0799wTpT7H3Ns4oZHCnXq7Y+N2vPoIjc8v45mNH8N6/+0/c8ch04veR1TgqQaGZYoJ2uWKr\nR/YNNmhO3bNXLA8darKC9vDsEsaHLIyVmtcoTI2XsFxxsFBW28vWjeyVDtXMxkkebEQj+6Nz7g10\nw0Tz2ZKkYBgZlF4mr7PPwgk4dHIJh04upf57gvRlNY7bn4MCfnB6Yv/9A8fx9989gNUjRX+6f/vD\n07jm5Rvx2PQczt88qfQ+crNnlV7qaSZoV6pqfjjQ2sZJ5Nkrin3FdmA7ItTeuBHNbJwXTy5jc4uo\nHnDFHgBmTq20vDEAyTcc18fGSVL6a3ivCY9d5kE2Kop9FjtV1fo0qW04DqTfG8d2BI6eWnFv9gmu\nl07py8heLuwoZHCnfvRFt9/5tx4/gu884/Za+dZjR/Endz2Bt/zFt/2ftyKp1wukV3qpYpEAXgvh\nZl0vE0T2ZcUEbdyWhM3GB8SXXrqrZ1uLUlDsVbCTNELTqM5eRvYqJYryMo2K/bQU+0nFyN5Mf6eq\nZL1x0g8OAeDY/ApsR6BiCxzPcHOlvhR7WRFQaMMPTsojnpjvf2EWz84sYNdZa3FqpYq/uvsZAMDd\nTxxVeh8Z2avQrMVsp6woVroA7qbjTW2cRM2n1P4zy95qWBUbx4/sY1bQHp5dblmJA7Qh9gnr7HVZ\nQZukQICIYpPLUuxVbZxsErTqlqJlxuciuo2cAQG1c5YFfSv2hYCNk6Zn/8iLs9jmLasHgN9447kY\nK1kYKZrYumYE9z45o/Q+iab/KSZoXc8+QYI2BRtnuWJjqYFHrrq5intMvGc/u1jB8YVy6HNrxLox\nV+xVKnL8/kYJVtBqs6gqQYEA4FlQkbFPzy1j9UhBadYFZNMIra0WxynfgKZZ7LtHVtU4i+Uqnj22\ngLdesgVbVg1j9UgBl5y2Ch//mfNx83WX4C0XbcIPnjuBU8uVlu9ltxHZp2PjqJdelpokaAsmKd28\n4m7IH/qHfXj3rd+LPT6JjVPbJzd843h6xi15275+rOV7rB4pwjRIKbKX4pcostdD6xMt6gPiy0aP\nzC23rG4KUjDSr7OXq+mVxN7IJkE7PVtLzB6ey07s+zJBW65GPPuUPLjHp09BCOD8zRM4Z8N5WCzb\nMAzCtZeeBgCYHC7gc/c8g289fhRvvGBj04i56gilpepAeglaIUTiOvvFcv1m4eWq+sIamTiTX7Af\nvXAS9zzhzoYOHl/EllXDOLVS+x2+jaMwRsMgFEyqi+yfPjoPQE3sTYOwdrSoJvYJrBAAfoJOB5Is\n6gPi9489PLuMjRMl5d8pG6EJITra5L0ZydZ8ZLOCdnpuBQXTvdEfyTCy70uxr9gOClbQs+/+F+r+\nZ17CPU+6fvz5mydw2up6S+CVZ6zGWMnCR/75QUwMWdjzmz+JyQY9XWzHUf+imelE9n4fesVpeNEy\ncHIp3sZR8UiBWsR16MQS7n7iKP7xgecxXDCxVLFx+8OH8a3Hj+KBZ4/7x1+6dRUAtcge8LYmrNSL\nfdEyYj+zOKbGS0ptjp2Ekb1ejdDUq3HkcdGxH5lbxkWnqVWfAbUbS8UWStUy7ZDExpEWVtp5hOnZ\nJawfH4IjRMi/T5u+Fftiip7900dP4ef+5gEAbvS+ZVX81LVoGbjl51+Je5+cwf+691l89+ljeJPX\nUTFKNUk1DqWToF1JEAUBTers2xD7j3/tET9q/+XdZ+PuJ2bwp3c9haWKjfdfcSa2rB7GVx88hB94\nG4Cri73hL6yRPH10HmetG1U+31OKq2j9yF4xSiXPxkkzslUlaWRvGeGyyXLVwbH5MjZOqNs4sq49\nzfLTiu1uxqPyWfv9ntK2cebc1tpVR+AI2zidIT37tBIu//6wu2jqprddgDPXjTb9or7m7HV41bY1\n+OIDz+O+p2Yair2doIFUsz1AOyFJ8tM9rplnn0zslysO3nzhRrz6zLV4xytPw1DBxM13PYlXn7kG\nv/3TLwMRoWQZ2P/CLAAo1dkDXnloNLKfmcclp69Wej3gJmmfmG69tF1+HEnq7AF31a3qhidpkWQF\nLSC3VKz9W4rWxskENo6M7B0Hw1D7PJOSJPDwq3EySNCev2USjiPw5JHsWiYonQUiupWI7ieiG5Mc\no/K6NJB19qZBXs/sLov9I9N4xdZVePflZ+CK7etaHl8wDbxm+1rc9+SxhsvuE0X2TRpRdYK/2XgX\nVtCqfsGC3v57d23De1+zDWMlC29/xRa8YusqfOLtF/g309edM+Ufq1J6Cbg3rqBnv1yx8cKJJWyf\nau3XS2Rk38pf9xO0isIt/+s6WDlJVtC6x1Eo2KiJfYIEbQaNx9oJPNL07IUQbmQ/MYQNE0M4Mqfe\nd6lTWp4FIroWgCmE2AXgLCLaoXKMyuvSIthTu9Dl/hsHjy/i4UNzeNMFGxO97nXnrMehk0t4ZiZ+\nIwzbTlCNIxO03Rb7qqxh77w3jnLnQ8+rXTdWxM5ta/znT18zgi//8hXY7u0LK587a91oojGWLCNU\njfPMzDyEUEvOSqbGSqjYArNLzauqkiZoqcmOT1kjhVt1dmlQOLJPunoWCJQQpyiuybpwppfjk8wt\nVbFccbBxcggbJ4cwv1JVqtbrBio2zm4At3mP7wTwWgBPKRzzCoXXdczj03P4lS/+MPTccy8t4gyv\nhrpgGrht70Hc/bja4qZWzHuVIW88P96OacSV57gzgPfc+r3YZfcvnlzC6WvUEoYyQfvX9z6Df/n+\nwUTjaMZKgvp4wBX74wtlXH3zvaHnD51cwg5FMZU35atfvlFJJK88ZwrPHltQXvhVsgzseeqYP8YF\n7/NLJPbewqq3f/Y7TaNEefNNul7iLX+xR7k2Py2OL7grOVXzDZZJuPORaVx9s5tDOendCJOIvRTX\n6295QDnQScqRuWWMFNXcaiK3bff/uf8AvvHQ4VTGI2cNGyaG/BndW//yO7jqZevxsbe8PJXfKVE5\nC6MADnmPjwO4VPGYlq8johsA3AAAW7duVR50kCHL9DeKluzYMIbrdp4OAPjvr9+O/S+cbOu9G3Hd\n+nF/f1JVTls9gl99w46GbU13bBjD7nPXK73XaNHEB193Fg4m2EFJlZ3bVuNV29T87J+5ZAuOxnSE\n3LFhDFe/fIPSe6wdLeJXfnI7fvaVpykd/74rtmF8yMLUmJo3/L4rzsSdj4Yb010zPpRI7F9z9lpc\ne+kWP6fRjItOm8SVO6ZaHgcAV71sPR55ca7ruZd22b5+XPlG9Ys/cRa+67UHkWxbO4qJYfU04BXb\n1+Ftl2xOddHjjg1juCwwY2zFr75hBx6fVmtx0i6XnrEaV2xfB0cI/7pSXXXcCdSqdSsR/TmAfxJC\nPOBZM+cJIT7Z6hgAG1q9LsjOnTvF3r17O/3/MAzDDBREtE8IsbPVcSpz4X1wLRgAuBjAAcVjVF7H\nMAzDZIDKnOsrAPYQ0WYAbwLwTiK6SQhxY5NjLgcgYp5jGIZhekDLyF4IMQc3AfsAgNcLIfZHhD7u\nmNm457o7dIZhGEYVpWyKEOIEapU1yseovI5hGIZJn77seskwDMOEYbFnGIYZAFjsGYZhBgAWe4Zh\nmAGg5aKqrCCiGQDPdfAW6wAca3kU0w58btODz226DML5PUMI0XLZtjZi3ylEtFdlFRmTHD636cHn\nNl34/NZgG4dhGGYAYLFnGIYZAPpJ7G/p9QD6GD636cHnNl34/Hr0jWfPMAzDNKafInuGYRimAX0n\n9kS0iYiuIqLx1kczSSGiNUR0NRG13nyXAaB+zvjcpsugn19txT66WTkRbSCiPS1ecw6AfwFwBYB7\niagY+NkFRHRXqoPOEcHzS0STRHQ7Ed1JRF8OnrfIa1YD+BqAywDcTURTgZ9tIKIfxr1u0Iic24bn\nLPIaPreKRLXBe67pOeLzq6nYx2xW/ioAn4e71WEzLgLwPiHE7wF4FsCZ3vsRgJsBFNIbdX6Inl8A\nvwDgZiHENQCmAbyxwUsvAvBrQohPALgD4a0mPw1gOLVB54SYc9vsnAXhc6tAjDbs8H7U6hwN/PnV\nUuxRv4H5awBcDyC0OSQRnUlEfyL/LYT4vwCeI6K3AFgN4GnvR+8DcHfKY84TuxE+v3NCCDnrmQJw\nFACIaJiI/lG+SAhxr7fN5JVwI6T7veN+EsAC3BvFoLMb4XN7VoNzxue2PXYjfH5fG3eO+PzWo6vY\nRzcrH47b/EQI8WMhxK9Hnh4DcB3c1guCiNYCeDfcuzfjEj2/GwCAiHYBWC2EeAAAhBBLQoh3BV/o\nzZKuB3ACQMWzfH4bwP/MaOy6U3duo+cM4HPbAdHzezpizhGf33p0Fft51KZVY0gwTiHESSHEe+Fa\nNq8C8IcAPiqEqHR9lPml7vwS0RoAnwHw/mYvFC4fBvAjAG+F+0X5rBDiZIrjzRN15zbmnMXC51aJ\n6PkFFM/RoJ9fXcW+rc3Kiehz3jQNAFYBOAngdQD+iIjuAXAJEd3U3aHmkuj5fQHAl+DeFBs2oyOi\n3ySin/f+Kc/vVQA+HDi/f5vaqPNB9NwWYs5ZHXxulYme33dD4Rzx+QUghNDuD4AJAPvhJlUfAzDp\nPX9P5LgzAfxJ5N/fBrAHwG/HvO89aY05T39izu9H4E5t7/H+XO8dNwzgHwOvWw3gLgD3AfgsvEV5\nfH6bntsz4s4Zn9uund/JuHPE57f+j7YraL1SqasB3CeE6PvkSdbw+U0PPrfpwue3PbQVe4ZhGKZ7\n6OrZMwzDMF2ExZ5hGGYAYLFnGIYZAFjsGYZhBgAWe4ZhmAHg/wORRAZUb6xnUAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dd0b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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i37NviSxonjYsa8W4DRcojYlsNQKlxeKxwr5QOLAc+7QFSl7H7py7X0QOALgM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\nCwoKCiaG4tgLCgoKJobi2AsKCgomhv8PkKuOjMlh7sUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e2add8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "th_sleep = 0.5 #将sleep/wake阈值设置为20\n",
    "th_deep = 0 #将deep/light-sleep阈值设置为10\n",
    "dic_D = {t: D_zcm[t] for t in range(t)}\n",
    "\n",
    "dic_score = dic_D.copy() #字典浅复制\n",
    "for i in range(4): #Webster's算法未计算的前4分钟状态同第5分钟\n",
    "    dic_score[i] = dic_score[4]\n",
    "for i in range(t-2,t): # 最后2分钟状态同前一分钟\n",
    "    dic_score[i] = dic_score[t-3]\n",
    "\n",
    "\n",
    "for key in dic_score.keys():\n",
    "    if  dic_score[key] > th_sleep:\n",
    "        dic_score[key] = 2\n",
    "    elif dic_score[key] > th_deep:\n",
    "        dic_score[key] = 1\n",
    "    else:\n",
    "        dic_score[key] = 0\n",
    "    # print(dic_score[key])\n",
    "    \n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_D.values())\n",
    "plt.show()\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_score.values())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FPFygpPn9puMt9OxSVeuyCpSAbhrJciY0BDpFdxx7mueMhVE4dmuBkt9GGw7H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\nyI49IyMjY2TIjj0jIyNjZPj/Ko4DR/z7SUEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14876d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bI0vh9dOOt3T8oqwVo9nopWFROkaBUmwbbaQ89g5oy7ftCm9FwES8dCiOvb2d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\n2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16903a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From 01:34 enter sleep monitoring program...\n"
     ]
    },
    {
     "data": {
      "image/png": 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S6kHe3sblAd4HI5YFFj6hLgJGRMcDeDIz/0Pt7X8CcDERPRvAk2H2/IyGKrLw3/dopIzY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x147cf048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  因刚开始入睡时，在PSG检测到进入睡眠状态1时，人体已经停止动作静止；故设置此重新评分法则：\n",
    "# (a) After at least 4 minutes scored as wake, the next 1 minute scored as sleep is rescored wake;\n",
    "# (b) after at least 10 minutes scored as wake, the next 3 minutes scored as sleep are rescored wake;\n",
    "# (c) after at least 15 minutes scored as wake, the next 4 minutes scored as sleep are rescored wake;\n",
    "# (d) 6 minutes or less scored as sleep surrounded by at least 10 minutes (before and after) scored as wake are rescored wake;\n",
    "# (e) 10 minutes or less scored as sleep surrounded by at least 20 minutes (before and after) scored as wake are rescored wake.       \n",
    "dic_rescore = dic_score.copy()\n",
    "for key in dic_rescore.keys(): # rule (a)\n",
    "    if (key > 3 and all([dic_rescore[key-i] == 2 for i in range(1,5)])and dic_rescore[key] < 2 ):\n",
    "        dic_rescore[key] = 2\n",
    "        break\n",
    "\n",
    "#         i = 1\n",
    "#         while(i<5):\n",
    "#             if dic_rescore[key-i] == 2:\n",
    "#                 i+=1\n",
    "#             else:\n",
    "#                 break\n",
    "#         if(i == 4):\n",
    "#              dic_rescore[key] = 2\n",
    "# plt.plot(array(range(t-6))/60, dic_rescore.values())\n",
    "# plt.show()    \n",
    "for key in dic_rescore.keys():# rule (b)\n",
    "    if (key > 9 and key < len(dic_rescore)-3 and all([dic_rescore[key - i] == 2 for i in range(1, 11)])and dic_rescore[key + 1] < 2):\n",
    "        for i in range(1, 4):\n",
    "            dic_score[key+i] = 2\n",
    "        break\n",
    "                \n",
    "for key in dic_rescore.keys():# rule (c)\n",
    "    if (key > 14 and key < len(dic_rescore)-4 and all([dic_rescore[key - i] == 2 for i in range(1, 16)])and dic_rescore[key + 1] < 2):\n",
    "        for i in range(1, 5):\n",
    "            dic_score[key+i] = 2\n",
    "        break\n",
    "                \n",
    "for key in dic_rescore.keys():# rule (d)\n",
    "    if (key > 9 and key < len(dic_rescore)-15 and all([dic_rescore[key-i] == 2 for i in range(1,11)])and dic_rescore[key + 1] < 2):\n",
    "        i=2\n",
    "        while(i<=6):\n",
    "            if dic_rescore[key+i] < 2:\n",
    "                i+=1\n",
    "            else:\n",
    "                break\n",
    "        if all([dic_rescore[key + i + ii] == 2 for ii in range(1,11)]):\n",
    "            for ii in range(1,i):\n",
    "                dic_rescore[key+i-ii] = 2\n",
    "                \n",
    "for key in dic_rescore.keys():# rule (e)\n",
    "    if (key > 19 and key < len(dic_rescore)-29 and all([dic_rescore[key-i] == 2 for i in range(1,21)])and dic_rescore[key + 1] < 2):\n",
    "        i=2\n",
    "        while(i<=10):\n",
    "            if dic_rescore[key+i] < 2:\n",
    "                i+=1\n",
    "            else:\n",
    "                break\n",
    "        if all([dic_rescore[key + i + ii] == 2 for ii in range(1,21)]):\n",
    "            for ii in range(1,i):\n",
    "                dic_rescore[key+i-ii] = 2\n",
    "# plt.subplot(211)\n",
    "plt.title(\"sleep score by ZCM\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_score.values())\n",
    "plt.show()\n",
    "                \n",
    "                \n",
    "plt.title(\"sleep rescore-1 after 5 gole rules\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_rescore.values())\n",
    "plt.show()\n",
    "\n",
    "# print(list(dic_rescore.values()).count(1))\n",
    "# print(dic_rescore.values())\n",
    "# key = 9\n",
    "# if ( key < len(dic_rescore)-60 and all([dic_rescore[key + i] < 2 for i in range(1,61)])):\n",
    "#     print(\"yes\")\n",
    "\n",
    "#判断是否进入睡眠程序:检测到连续30/60分钟睡眠才进入睡眠监测程序——待模块化！！！\n",
    "\n",
    "monitor = 0\n",
    "\n",
    "for key in dic_rescore.keys():\n",
    "    if ( key < len(dic_rescore)-60 and all([dic_rescore[key + i] < 2 for i in range(1,61)])):\n",
    "        for tt in range(1,key):\n",
    "            dic_rescore[key - tt] == 2 \n",
    "        print(\"From\", m2t(m_start+key), \"enter sleep monitoring program...\")\n",
    "        monitor = 1\n",
    "        break\n",
    "if(monitor == 0):\n",
    "    dic_rescore = {t: 2 for t in range(t)}\n",
    "\n",
    "# 深睡浅睡规则待完善 ！！！\n",
    "\n",
    "\n",
    "# plt.subplot(212)\n",
    "plt.title(\"sleep rescore-2 after sleep monitoring program\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_rescore.values())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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S6kHe3sblAd4HI5YFFj6hLgJGRMcDeDIz/0Pt7X8CcDERPRvAk2H2/IyGKrLw3/dopIzY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1203e320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to fall sleep: 01:34\n",
      "Time of sleep: 3.50 hours\n",
      "Time of deep sleep: 2.02 hours\n",
      "Time of light sleep: 1.48 hours\n"
     ]
    },
    {
     "data": {
      "image/png": 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lx+6MUIunmlQlqTOWlBctPw4XsWtoEXs/XSjPUAqUpFHuOFfcMliEZuehmQpr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\nswp/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11891710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplot(212)\n",
    "plt.title(\"sleep rescore-2 after sleep monitoring program\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_rescore.values())\n",
    "plt.show()\n",
    "\n",
    "# 睡眠指标统计：入睡时间time_fall， 起床时间time_get， 睡眠总时长t_sleep, 深睡眠时长t_deep， 浅睡眠时长t_light，睡眠过程中清醒时长t_wake\n",
    "\n",
    "\n",
    "#如果检测点前10分钟清醒,检测点后10分钟睡眠，这判定为入睡时间点\n",
    "for key in dic_rescore.keys():\n",
    "    if dic_rescore[key]<2:\n",
    "        if (key > 9 and key < len(dic_rescore)-10 and all([dic_rescore[key-i] == 2 for i in range(1,11)]) and all([dic_rescore[key+i] < 2 for i in range(1,11)])):\n",
    "            time_fall = m2t(key+m_start)\n",
    "            print(\"Time to fall sleep:\", time_fall)\n",
    "            break\n",
    "        if (key < 9 or key > len(dic_rescore)-10):\n",
    "            time_fall = m2t(key+m_start)\n",
    "            print(\"Time to fall sleep:\", time_fall)\n",
    "            break\n",
    "# 相反的，如果检测点前10分钟睡眠，检测点后10分钟清醒，则判定为起床时间点\n",
    "for key in dic_rescore.keys():\n",
    "    if (key > 9 and key < len(dic_rescore)-10 and dic_rescore[key] == 2 and all([dic_rescore[key-i] < 2 for i in range(1,11)]) and all([dic_rescore[key+i] == 2 for i in range(1,11)])):\n",
    "        time_get = m2t(key+m_start)\n",
    "        print(\"Time to get up:\", time_get, \",and out of sleep monitoring program.\")\n",
    "        #退出睡眠监测程序，并重新检测入睡时间点，即起床后——未进入睡眠程序前的点赋值为2，此模块待完善！！！\n",
    "        for kk in range(key, t):\n",
    "            dic_rescore[kk] = 2\n",
    "#           print(dic_rescore[key])\n",
    "        break\n",
    "\n",
    "# 睡眠过程中的清醒时长，如果检测点前10分钟睡眠，清醒时间段后10分钟睡眠，则判定为睡眠过程中清醒 \n",
    "for key in dic_rescore.keys():\n",
    "    if (key > 9 and key < len(dic_rescore)-10 and dic_rescore[key] == 2 and all([dic_rescore[key-i] < 2 for i in range(1,11)])):\n",
    "        wake_in_sleep = 0\n",
    "        while (key+wake_in_sleep < len(dic_rescore)-10):\n",
    "            if (dic_rescore[key+wake_in_sleep] == 2):\n",
    "                wake_in_sleep+=1\n",
    "            else:\n",
    "                break\n",
    "        if all([dic_rescore[key+wake_in_sleep+i] < 2 for i in range(1,10)]):\n",
    "            print(\"Wake time during sleep:\", m2t(key+wake_in_sleep+m_start))\n",
    "            print(\"The length of wake time during sleep:\", wake_in_sleep,\"minutes\")\n",
    "     \n",
    "t_deep = list(dic_rescore.values()).count(0)/60\n",
    "t_light = list(dic_rescore.values()).count(1)/60\n",
    "t_wake = list(dic_rescore.values()).count(2)/60\n",
    "t_sleep = t_deep + t_light\n",
    "\n",
    "\n",
    "print(\"Time of sleep:\", '%.2f' % t_sleep, \"hours\")\n",
    "print(\"Time of deep sleep:\", '%.2f' % t_deep,\"hours\")\n",
    "print(\"Time of light sleep:\", '%.2f' % t_light,\"hours\")\n",
    "# print(\"Time of wake:\", '%.2f' % t_wake,\"hours\")\n",
    "\n",
    "plt.title(\"sleep rescore-3 after get-up monitoring program\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_rescore.values())\n",
    "plt.show()"
   ]
  }
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